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      "metadata": {
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          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=024ee964caf34d8740f4164fae9a0d30396965fb55efdad00a14841277a1f0bf\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.99\n",
            "Collecting portalocker>=2.0.0\n",
            "  Downloading portalocker-2.8.2-py3-none-any.whl (17 kB)\n",
            "Installing collected packages: portalocker\n",
            "Successfully installed portalocker-2.8.2\n",
            "Collecting pytorch-lightning\n",
            "  Downloading pytorch_lightning-2.1.2-py3-none-any.whl (776 kB)\n",
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            "Installing collected packages: lightning-utilities, torchmetrics, pytorch-lightning\n",
            "Successfully installed lightning-utilities-0.10.0 pytorch-lightning-2.1.2 torchmetrics-1.2.1\n"
          ]
        }
      ],
      "source": [
        "!pip install 'sentence-transformers'\n",
        "!pip install 'portalocker>=2.0.0'\n",
        "!pip install 'pytorch-lightning'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "import numpy as np\n",
        "\n",
        "# Load a pre-trained BERT model\n",
        "model = SentenceTransformer('bert-base-nli-mean-tokens')  # You can choose a different pre-trained model\n",
        "\n",
        "# Example sentences\n",
        "sentences = [\n",
        "    \"Let's train a classifier with BERT.\",\n",
        "    \"BERT is a powerful language model.\"\n",
        "]\n",
        "\n",
        "# Get sentence embeddings\n",
        "sentence_embeddings = model.encode(sentences)\n",
        "\n",
        "# Print sentence embeddings\n",
        "for sentence, embedding in zip(sentences, sentence_embeddings):\n",
        "    print(f\"Sentence: {sentence}\")\n",
        "    print(f\"Embedding: {np.round(embedding, 6)}\\n\")\n"
      ],
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          "text": [
            "Sentence: Let's train a classifier with BERT.\n",
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            " -1.682300e-01  2.029510e-01  7.222330e-01 -6.072660e-01 -1.290600e-01\n",
            "  5.930370e-01  5.406480e-01  1.477700e-02  4.569380e-01 -4.850980e-01\n",
            " -1.404140e-01  2.576050e-01  5.628900e-02  2.488700e-01 -7.363950e-01\n",
            " -1.653550e-01  5.716370e-01 -1.802400e-02 -1.049216e+00 -1.048555e+00\n",
            "  4.770440e-01 -7.495700e-02 -4.263100e-01  6.048290e-01  5.960250e-01\n",
            "  1.952660e-01 -6.250080e-01 -6.912400e-02 -5.383680e-01 -8.861190e-01\n",
            " -4.042300e-01  8.616700e-02  2.209730e-01 -3.620740e-01 -1.098530e-01\n",
            " -6.576100e-01  3.241300e-02  3.027870e-01 -5.853210e-01  7.146260e-01\n",
            "  1.310600e-01  3.739580e-01  5.660270e-01  5.043150e-01 -4.714550e-01\n",
            "  3.348510e-01 -1.221702e+00  6.185160e-01 -4.406200e-02 -6.254830e-01\n",
            " -6.167990e-01 -1.322220e-01 -1.880300e-02  7.555500e-02 -2.255500e-01\n",
            " -1.526190e-01 -2.892410e-01 -7.749900e-02]\n",
            "\n",
            "Sentence: BERT is a powerful language model.\n",
            "Embedding: [-3.878940e-01  2.313850e-01  1.397652e+00  2.383300e-01 -2.112790e-01\n",
            " -4.378150e-01 -2.453750e-01  7.278100e-02  1.295249e+00 -2.425610e-01\n",
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            "  6.109040e-01 -2.410100e-01  8.532300e-02 -6.949390e-01  6.479790e-01\n",
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            " -5.439400e-02 -6.728270e-01 -3.414000e-01 -5.497090e-01 -2.760220e-01\n",
            "  3.985740e-01 -1.520794e+00 -4.850050e-01  2.428670e-01  2.048120e-01\n",
            " -1.135676e+00 -1.219582e+00 -3.928100e-01]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jIAQHP3iOzB",
        "outputId": "b6bd3d00-61a6-47ed-fe69-a9639b68e36e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768,)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from tqdm import tqdm\n",
        "import torchtext.datasets as datasets\n",
        "from collections import Counter"
      ],
      "metadata": {
        "id": "6W8pRWBY3oyD"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = datasets.IMDB('./', split=\"train\")\n",
        "test = datasets.IMDB('./', split=\"test\")"
      ],
      "metadata": {
        "id": "nt__kJlVXHb5"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = []\n",
        "for label, text in train:\n",
        "  train_data.append((label, text))\n",
        "print(\"Label Counts for Train Data: \", Counter([_[0] for _ in train_data]))\n",
        "\n",
        "test_data = []\n",
        "for label, text in test:\n",
        "  test_data.append((label, text))\n",
        "print(\"Label Counts for Train Data: \", Counter([_[0] for _ in test_data]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2STf0x9XJdI",
        "outputId": "c3d41599-84de-428d-e948-4092d076dc5f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Counts for Train Data:  Counter({1: 12500, 2: 12500})\n",
            "Label Counts for Train Data:  Counter({1: 12500, 2: 12500})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_sentence_embeddings = model.encode([_[1] for _ in train_data])\n",
        "test_sentence_embeddings = model.encode([_[1] for _ in test_data])"
      ],
      "metadata": {
        "id": "zjw8_TVcXLqv"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "ohe_enc = OneHotEncoder()\n",
        "train_label_raw = [[sample[0]] for sample in train_data]\n",
        "test_label_raw = [[sample[0]] for sample in test_data]\n",
        "\n",
        "ohe_enc.fit(train_label_raw + test_label_raw)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 95
        },
        "id": "8Z0ytBu7bcCi",
        "outputId": "447ddd50-5331-4749-90c4-65213e04e3a4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneHotEncoder()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneHotEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_label = ohe_enc.transform(train_label_raw)\n",
        "test_label = ohe_enc.transform(test_label_raw)"
      ],
      "metadata": {
        "id": "svnCVtshb2Lw"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels_tensor = torch.from_numpy(train_label.todense().astype('float32'))\n",
        "test_labels_tensor = torch.from_numpy(test_label.todense().astype('float32'))"
      ],
      "metadata": {
        "id": "25v1AwHJb7oH"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchmetrics\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "import torch.nn.functional as F\n",
        "import pytorch_lightning as pl\n",
        "\n",
        "class Net(pl.LightningModule):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.criterion = torch.nn.BCELoss()\n",
        "        self.train_acc = torchmetrics.classification.Accuracy(task=\"multiclass\", num_classes=2)\n",
        "        self.valid_acc = torchmetrics.classification.Accuracy(task=\"multiclass\", num_classes=2)\n",
        "        self.fc1 = nn.Linear(768, 120)  # input dimension with 2000 words/tokens\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.fc1(x))\n",
        "        x = F.relu(self.fc2(x))\n",
        "        x = F.sigmoid(self.fc3(x))\n",
        "        return x\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        logits = self.forward(x)\n",
        "        loss = self.criterion(logits, y)\n",
        "        accuracy = self.train_acc(logits.round(), y)\n",
        "        self.log('train_loss', loss)\n",
        "        self.log('train_acc', accuracy, on_epoch=True)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        logits = self.forward(x)\n",
        "        loss = self.criterion(logits, y)\n",
        "        accuracy = self.valid_acc(logits.round(), y)\n",
        "        self.log('val_loss', loss)\n",
        "        self.log('val_acc', accuracy, on_epoch=True)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        opt = torch.optim.Adam(self.parameters(), lr=1e-3)\n",
        "        return opt\n",
        "\n",
        "net = Net()\n",
        "print(net)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VYzbWP_aXVtg",
        "outputId": "f05b0a2d-c6eb-4789-9615-8649278b2026"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Net(\n",
            "  (criterion): BCELoss()\n",
            "  (train_acc): MulticlassAccuracy()\n",
            "  (valid_acc): MulticlassAccuracy()\n",
            "  (fc1): Linear(in_features=768, out_features=120, bias=True)\n",
            "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
            "  (fc3): Linear(in_features=84, out_features=2, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(Dataset):\n",
        "  def __init__(self, X, y):\n",
        "    self.X = X\n",
        "    self.y = y\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    return self.X[idx], self.y[idx]\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.X)\n",
        "\n",
        "train_dataset = CustomDataset(train_sentence_embeddings, train_labels_tensor)\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=128, shuffle=True)\n",
        "\n",
        "test_dataset = CustomDataset(test_sentence_embeddings, test_labels_tensor)\n",
        "test_dataloader = DataLoader(test_dataset, batch_size=128, shuffle=True)"
      ],
      "metadata": {
        "id": "bxfoMLJfamAb"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample = next(iter(train_dataloader))"
      ],
      "metadata": {
        "id": "Nn3nvWy-a1Aq"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample[0].shape, sample[1].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXBdUpqMbAkI",
        "outputId": "06df5812-7bf7-49be-d9e4-0b3d6ca66423"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([128, 768]), torch.Size([128, 2]))"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
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        },
        "id": "Mgsz_AAJbBP5",
        "outputId": "0865061a-156a-4bd8-e7fc-f5454d290fe1"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
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              "┃\u001b[1m \u001b[0m\u001b[1m     Validate metric     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
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              "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.4983200132846832    \u001b[0m\u001b[35m \u001b[0m│\n",
              "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.6932352185249329    \u001b[0m\u001b[35m \u001b[0m│\n",
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              "┃<span style=\"font-weight: bold\">      Validate metric      </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
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              "│<span style=\"color: #008080; text-decoration-color: #008080\">          val_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.4983200132846832     </span>│\n",
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              "└───────────────────────────┴───────────────────────────┘\n",
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            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name      | Type               | Params\n",
            "-------------------------------------------------\n",
            "0 | criterion | BCELoss            | 0     \n",
            "1 | train_acc | MulticlassAccuracy | 0     \n",
            "2 | valid_acc | MulticlassAccuracy | 0     \n",
            "3 | fc1       | Linear             | 92.3 K\n",
            "4 | fc2       | Linear             | 10.2 K\n",
            "5 | fc3       | Linear             | 170   \n",
            "-------------------------------------------------\n",
            "102 K     Trainable params\n",
            "0         Non-trainable params\n",
            "102 K     Total params\n",
            "0.410     Total estimated model params size (MB)\n"
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              "┃\u001b[1m \u001b[0m\u001b[1m     Validate metric     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
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              "│\u001b[36m \u001b[0m\u001b[36m         val_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8178399801254272    \u001b[0m\u001b[35m \u001b[0m│\n",
              "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.4495897889137268    \u001b[0m\u001b[35m \u001b[0m│\n",
              "└───────────────────────────┴───────────────────────────┘\n"
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              "┃<span style=\"font-weight: bold\">      Validate metric      </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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}